ISPRS International Journal of Geo-Information

27 pages, 20171 KiB

Open AccessArticle

An Approach to Selecting an E-Commerce Warehouse Location Based on Suitability Maps: The Case of Samara Region

by Sergey Sakulin, Alexander Alfimtsev and Nikita Gavrilov

ISPRS Int. J. Geo-Inf. 2025, 14(9), 326; https://doi.org/10.3390/ijgi14090326 (registering DOI) - 24 Aug 2025

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide [...] Read more.

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

► Show Figures

Figure 1

29 pages, 3017 KiB

Open AccessArticle

Enhancing Electric Vehicle Charging Infrastructure Planning with Pre-Trained Language Models and Spatial Analysis: Insights from Beijing User Reviews

by Yanxin Hou, Peipei Wang, Zhuozhuang Yao, Xinqi Zheng and Ziying Chen

ISPRS Int. J. Geo-Inf. 2025, 14(9), 325; https://doi.org/10.3390/ijgi14090325 (registering DOI) - 24 Aug 2025

Abstract

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user [...] Read more.

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

33 pages, 4109 KiB

Open AccessArticle

National Spatial Data Infrastructure as a Catalyst for Good Governance and Policy Improvements in Pakistan

by Munir Ahmad, Asmat Ali, Muhammad Nawaz, Farha Sattar and Hammad Hussain

ISPRS Int. J. Geo-Inf. 2025, 14(9), 324; https://doi.org/10.3390/ijgi14090324 (registering DOI) - 24 Aug 2025

Abstract

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis [...] Read more.

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis of 23 national policy frameworks and a stakeholder survey (n = 28). The results reveal that while many policies reference spatial data conceptually, critical components such as standardised datasets, spatial dashboards, and institutional coordination mechanisms remain underdeveloped. Spatial references are largely confined to early policy stages, with limited integration in evaluation and maintenance, thereby limiting adaptive governance. Conversely, survey findings reflect strong recognition of NSDI’s value across governance principles, policy integration, and spatial awareness dimensions. The composite endorsement score highlights institutional demand for geospatial tools, data standards, and capacity-building platforms. The study concludes that embedding NSDI within policy and planning systems can bridge critical governance gaps, enhance implementation fidelity, and support inter-agency coordination for long-term policy effectiveness. Full article

► Show Figures

Figure 1

16 pages, 2578 KiB

Open AccessArticle

Determination of the Solar Angle of Incidence Using an Equivalent Surface and the Possibility of Applying This Approach in Geosciences and Engineering

by Marián Jenčo

ISPRS Int. J. Geo-Inf. 2025, 14(9), 323; https://doi.org/10.3390/ijgi14090323 (registering DOI) - 23 Aug 2025

Abstract

The solar angle of incidence is the angle between the sunlight and the normal on the impact surface. The lower the angle of incidence, the more sun radiation the surface can absorb. There are several methods for calculating of this angle. Determining the [...] Read more.

The solar angle of incidence is the angle between the sunlight and the normal on the impact surface. The lower the angle of incidence, the more sun radiation the surface can absorb. There are several methods for calculating of this angle. Determining the geographical location of the equivalent surface is one of the lesser-known options. The equivalent surface is a tangential plane to the Earth that is parallel to a reference inclined surface. The geographical coordinates of the point of tangency are clearly determined by the slope and aspect. Since the equivalent surface is horizontal, basic solar geometry equations apply. Unlike the conventional equations commonly used today, they provide easily interpretable results. The sunrise and sunset times for an inclined surface and the time of an extreme incidence angle can be calculated directly. Approximate calculations are not necessary. In addition, the geographical approach allows for the hour angle to be determined, as well as the tilt for a given azimuth of the solar panel that is perpendicular to direct sunlight. This new procedure sets the time for regular changes in the horizontal direction of the sun-tracker. The renaissance of the geographical approach for calculating the temporal characteristics, which allows for the use of simple equations and the interpretation of their results, can also benefit agriculture, forestry, land management, botany, architecture, and other sectors and sciences. Full article

► Show Figures

Figure 1

20 pages, 4720 KiB

Open AccessArticle

Dynamic Optimization of Emergency Infrastructure Layouts Based on Population Influx: A Macao Case Study

by Zhen Wang, Zheyu Wang, On Kei Yeung, Mengmeng Zheng, Yitao Zhong and Sanqing He

ISPRS Int. J. Geo-Inf. 2025, 14(9), 322; https://doi.org/10.3390/ijgi14090322 (registering DOI) - 23 Aug 2025

Abstract

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic [...] Read more.

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

(This article belongs to the Topic The Use of Big Data in Public Health Research and Practice)

► Show Figures

Figure 1

21 pages, 2655 KiB

Open AccessArticle

A Hybrid Approach for Geo-Referencing Tweets: Transformer Language Model Regression and Gazetteer Disambiguation

by Thomas Edwards, Padraig Corcoran and Christopher B. Jones

ISPRS Int. J. Geo-Inf. 2025, 14(9), 321; https://doi.org/10.3390/ijgi14090321 - 22 Aug 2025

Abstract

Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. [...] Read more.

Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. However, obtaining significant volumes of geo-referenced data from Twitter, recently renamed X, can be difficult. Further, existing language modelling approaches can require the division of a given area into a grid or set of clusters, which can be dataset-specific and challenging for location prediction at a fine-grained level. Regression-based approaches in combination with deep learning address some of these challenges as they can assign coordinates directly without the need for clustering or grid-based methods. However, such approaches have received only limited attention for the geo-referencing task. In this paper, we adapt state-of-the-art neural network models for the regression task, focusing on geo-referencing wildlife Tweets where there is a limited amount of data. We experiment with different transfer learning techniques for improving the performance of the regression models, and we also compare our approach to recently developed Large Language Models and prompting techniques. We show that using a location names extraction method in combination with regression-based disambiguation, and purely regression when names are absent, leads to significant improvements in locational accuracy over using only regression. Full article

► Show Figures

Figure 1

21 pages, 6814 KiB

Open AccessArticle

Urban Land Subsidence Analyzed Through Time-Series InSAR Coupled with Refined Risk Modeling: A Wuhan Case Study

by Lv Zhou, Liqi Liang, Quanyu Chen, Haotian He, Hongming Li, Jie Qin, Fei Yang, Xinyi Li and Jie Bai

ISPRS Int. J. Geo-Inf. 2025, 14(9), 320; https://doi.org/10.3390/ijgi14090320 - 22 Aug 2025

Abstract

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy [...] Read more.

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies. Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation, 2nd Edition)

► Show Figures

Figure 1

Journal Menu

Journal Browser

ISPRS Int. J. Geo-Inf., Volume 14, Issue 9 (September 2025) – 7 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI